Modern cloud environments are dynamic and complex. Organizations continue to adopt new services and applications. This leads to a hidden risk: shadow data. These unmanaged data assets often pile up across cloud platforms without oversight. If ignored, they increase your attack surface. They also expose sensitive information to threats. Managing shadow data is crucial for a secure cloud environment.

This article explains how DSPM acts as a detective, pinpointing your hidden data. We’ll show you how it reveals these blind spots, so you can address them.

What Is Shadow Data?

Shadow data refers to untracked and unmonitored information hidden within an organization’s cloud ecosystem. It accumulates organically without malicious intent but can carry significant risks. Even if companies are unaware of it, they remain responsible for managing this data.

Common Sources of Unmanaged Data Assets

This data doesn’t appear out of nowhere. It often comes from well-intentioned projects and standard development processes. Common sources include developer sandboxes and old test environments. These environments can leave residual data behind. Outdated application versions contribute further. Additionally, misconfigured storage services pose major data security risks to businesses.

Why Shadow Data Expands Your Data Attack Surface

The essence of the issue is simple: you cannot defend what you do not see. Shadow data expands your attack area. This generates extra entry points for threats. It often includes sensitive data, including personal or intellectual property. This may result in the violation of regulations such as GDPR or HIPAA. Shadow data also causes blind spots for the security teams. As a result, many threat detection and response efforts fail to address unknown risks.

Understanding DSPM: A Framework for Data Visibility and Control

It’s paramount for security teams to understand DSPM as a framework for continuous visibility and control. This ensures they can effectively manage and safeguard all data assets. DSPM’s main aim is to ensure ongoing oversight wherever the data resides.

Core Capabilities of a DSPM Solution

A robust DSPM framework is built on several key capabilities. It automates data discovery across the whole multi-cloud setup. It then classifies and catalogs data based on its sensitivity and context. Finally, it conducts frequent evaluations and oversight of the security of data assets. It examines configurations, access rights, and vulnerabilities. This gives a clear picture of potential risks.

How DSPM Differs from Traditional Cloud Security Tools

It is important to understand how DSPM complements existing tools. Cloud Security Posture Management (CSPM) is an excellent tool for identifying network misconfigurations. Cloud Infrastructure Entitlement Management (CIEM) manages user and service identities. DSPM, however, completes the picture by focusing directly on the data. It answers the critical questions that others do not:

1. What data do I have?
2. Where exactly is it?
3. Who can access it?
4. Is the data properly configured?

This data-focused perspective makes DSPM the missing layer for true data-centric security. It uniquely equips organizations to uncover and secure shadow data that other security tools cannot detect or manage as effectively.

The Process of Uncovering Your Shadow Data

The journey from blind spots to clarity unfolds in a structured way. DSPM uses a cycle of discovery, analysis, and remediation. This approach simplifies a big problem into a continuous security task.

Step One: Automated Discovery and Data Classification

The first step is comprehensive visibility. DSPM tools scan without agents. They automatically and continuously map data stores in IaaS, PaaS, and SaaS environments. They then conduct a thorough content analysis to categorize data based on sensitivity. This classification utilizes labels such as Public, Internal, Confidential, and PII. These labels provide clear context about the value and risk of each asset found.

Step Two: Mapping Data Flow and Access Permissions

After finding and classifying the data, we need to understand its context. DSPM tracks data movement and maps user, service account, and application access permissions. Such mapping can help identify overprivileged accounts and excessive permissions. Data breaches and insider data security threats can be reduced by mitigating the risks you discover.

Step Three: Prioritizing Risks and Remediating Vulnerabilities

Contemporary DSPM tools help teams focus on what is most important. Their priorities depend on the sensitivity of the data and the intensity of the misconfiguration. For example, a publicly accessible bucket containing PII would be a priority. The system offers specific guidelines for troubleshooting problems. This may involve modifying access controls or applying encryption to ensure data security. It can also include securely archiving data that is no longer required.

Benefits of Proactive Shadow Data Management

Taking control of shadow data goes beyond technical details. It’s a strategic move. A proactive approach makes data security a competitive advantage. It also builds resilience and trust.

Drastically Reducing Your Data Attack Surface

This is the most immediate and significant benefit. Discovering and securing shadow data addresses hidden vulnerabilities. It reduces your data footprint and strengthens your organization against potential attacks.

Achieving and Maintaining Regulatory Compliance

Data privacy regulations require knowledge and control over personal data. A 2023 Gartner report highlighted that data security frameworks like DSPM are essential for reducing risks from unprotected data. DSPM offers continuous data security audit evidence. It monitors the location of sensitive data, the individuals authorized to access it, and the security measures. This makes it easier to meet PCI DSS, among other data regulations.

Empowering DevOps with Secure Data Practices

Shadow data management isn’t meant to block innovation. Instead, it should enable safe innovation. DSPM gives developers a clear view of the data landscape. This helps them avoid security risks. It also integrates data security into the development process. As a result, it fosters a culture in which everyone shares responsibility.

Frequently Asked Questions About DSPM and Shadow Data

1. Can’t I Find Shadow Data with My Existing Cloud Tools?

Native cloud tools provide basic visibility. However, they lack a cross-platform approach needed for complete shadow data discovery. They also lack continuous data classification and multi-cloud security assessments.

2. Is DSPM Only Relevant for Large Enterprises?

No. Any organization using cloud services can accumulate shadow data. The risk grows with cloud usage, not company size. A small startup can have unmanaged data from its SaaS apps and cloud databases. This may exceed the amount held by a large enterprise with formal processes.

3. How Often Should We Scan for Shadow Data?

Constant cloud changes require real-time discovery as opposed to periodic scans. This identifies new shadow data quickly, ensuring current risk assessments.

Conclusion

Shadow data is a frequent byproduct of cloud agility. However, it does not have to become a significant risk. Leveraging Data Security Posture Management enables organizations to discover and secure hidden assets effectively.

Addressing shadow data reduces your attack surface and enhances security. DSPM facilitates regulatory compliance. It also establishes a robust foundation for long-term data protection. Take the first step now: assess your cloud for shadow data and implement DSPM to protect against hidden risks.